Volume visualization of such medical image data sets provides a way to review anatomy in 3D/4D. Powerful tools of volume visualization methods use various intensity classifiers to select tissues and structures in the data set and map each intensity to a corresponding color, opacity, and material property values (e.g. diffuseness, shininess, reflection, refraction, transmittance, emittance, phase functions).
According to one advanced volume visualization method such as path tracing, the classified values are accumulated along a complex path based on the utilized material properties interacting with a lighting model to reveal a much more photorealistic perception about the contained tissues and organs. The method can also be used with another simpler volume visualization method such as ray casting, where the classified values are accumulated along a viewing ray and as a result the accumulated colour-value and/or opacity-values are perceived on the visualized surface of the objects.
Parts inside each classified tissue or structure, such as a tumor embedded inside an organ, are additionally revealed by defining a region of interest and removing the occluding regions outside of it. A simple clipping plane, for example, is particularly effective and efficient. A clipping plane define a half space as a region of interest while the region in the other half space is clipped.
The position and/or orientation of the clipping plane determines the part of the image data set that is visualised. Thus, a user can navigate through moving the plane to review the internals inside organs. However, simultaneously, the clipping plane also clips away parts of the structures of interest, and consequently the user loses the information of the relative positioning of multiple structures, e.g. the vessels surrounding the organ. However, this information is of special interest, since the position of the vessels next or near to the organ restricts the freedom of action during a surgery.
Alternatively, for simultaneously displaying multiple parts and structures, for example the tumors and the respective feeding vessels, the state of the art suggests a segmentation. That means each structure of interest is explicitly determined for each voxel. However, this method is tedious and time consuming since it is difficult to automate segmentation of abnormal, diseased anatomy with a resolution often not sufficient to extract fine vessels. Moreover, a manual editing is needed, when the segmentation fails.